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Title: A system to detect cerebral aneurysms in multimodality angiographic data sets

Abstract

Purpose: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors’ system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). Methods: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized by domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FP{sub DS}) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FP{sub DS}. For CTA, we reach a sensitivity of 95% at 22.8 FP{sub DS}. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training.more » 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.« less

Authors:
 [1]; ; ; ;  [2]
  1. Department of Simulation and Graphics, University of Magdeburg, 39106 Magdeburg (Germany)
  2. Institute for Neuroradiology, University Hospital of Magdeburg, 39120 Magdeburg (Germany)
Publication Date:
OSTI Identifier:
22409595
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 41; Journal Issue: 9; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 60 APPLIED LIFE SCIENCES; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; BLOOD VESSELS; COMPUTERIZED TOMOGRAPHY; MAGNETIC RESONANCE; TIME-OF-FLIGHT METHOD

Citation Formats

Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com, Tönnies, Klaus D., Beuing, Oliver, Paukisch, Harald, Scherlach, Cordula, and Skalej, Martin. A system to detect cerebral aneurysms in multimodality angiographic data sets. United States: N. p., 2014. Web. doi:10.1118/1.4890775.
Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com, Tönnies, Klaus D., Beuing, Oliver, Paukisch, Harald, Scherlach, Cordula, & Skalej, Martin. A system to detect cerebral aneurysms in multimodality angiographic data sets. United States. https://doi.org/10.1118/1.4890775
Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com, Tönnies, Klaus D., Beuing, Oliver, Paukisch, Harald, Scherlach, Cordula, and Skalej, Martin. 2014. "A system to detect cerebral aneurysms in multimodality angiographic data sets". United States. https://doi.org/10.1118/1.4890775.
@article{osti_22409595,
title = {A system to detect cerebral aneurysms in multimodality angiographic data sets},
author = {Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com and Tönnies, Klaus D. and Beuing, Oliver and Paukisch, Harald and Scherlach, Cordula and Skalej, Martin},
abstractNote = {Purpose: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors’ system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). Methods: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized by domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FP{sub DS}) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FP{sub DS}. For CTA, we reach a sensitivity of 95% at 22.8 FP{sub DS}. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.},
doi = {10.1118/1.4890775},
url = {https://www.osti.gov/biblio/22409595}, journal = {Medical Physics},
issn = {0094-2405},
number = 9,
volume = 41,
place = {United States},
year = {Mon Sep 15 00:00:00 EDT 2014},
month = {Mon Sep 15 00:00:00 EDT 2014}
}